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An Algorithm for Automatic Text Annotation for Named Entity Recognition Using Spacy Framework

openalex(2023)

ICAR-Indian Agricultural Statistics Research Institute

Cited 0|Views17
Abstract
Abstract Text Annotation is the process of adding metadata in the text and used in various tasks like natural language processing (NLP) and machine learning models. Named entity recognition (NER) is one of the interesting and challenging tasks of NLP and is being used extensively in many domains. The application of NER will also be useful in handling documents, queries, reports and research articles related to agriculture in identifying pests affecting crops. SpaCy, a free and open source library is being used for NER that requires the text data in a complex annotated format. The process of manual annotation is difficult and time-consuming task. Therefore, to streamline the process of text annotation, we developed an algorithm and a tool for automatic annotation of text data. Approximately 3.6 million queries were collected from “Kisan Call Centre”, a helpline service to farmers by Government of India and plant protection queries of Paddy and Wheat crops were extracted from this database. These queries were annotated with the help of developed tool and annotated corpus was created. The annotated corpus is used to develop NER models and trained for crops and associated pests identification in agriculture domain. Further, the performance of the model is enhanced by reducing features using plural to singular conversion and synonym substitution. The model achieved an F1-score of 97.20%, demonstrating a significant improvement of 3.01% compared to the performance with original queries.
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Named Entity Recognition,Textual Data,Text Classification
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要点】:本文提出了一种基于Spacy框架的自动文本标注算法,用于农业领域命名实体识别,有效提升了模型性能。

方法】:利用Spacy框架,开发了一套自动文本标注工具,通过将复数转换为单数和同义词替换减少特征,从而提高NER模型的表现。

实验】:收集了印度政府“Kisan Call Centre”的约360万查询数据,提取了水稻和小麦作物的植物保护查询,使用开发的工具进行标注,构建了标注语料库,该语料库用于训练NER模型识别农业领域的作物和关联害虫,模型达到了97.20%的F1分数,相比原始查询数据,性能提升了3.01%。